Bottom Line:
The size and intensity pattern of the template is automatically adapted to the input data so that the method is scale invariant and generic.Furthermore, the template matching threshold is iteratively optimized to ensure that the final skeletonized network obeys a universal property of voxelized random line networks, namely, solid-phase voxels have most likely three solid-phase neighbors in a 3 x 3 x 3 neighborhood.This optimization criterion makes our method free of user-defined parameters and the output exceptionally robust against imaging noise.

ABSTRACTWe present a method to reconstruct a disordered network of thin biopolymers, such as collagen gels, from three-dimensional (3D) image stacks recorded with a confocal microscope. The method is based on a template matching algorithm that simultaneously performs a binarization and skeletonization of the network. The size and intensity pattern of the template is automatically adapted to the input data so that the method is scale invariant and generic. Furthermore, the template matching threshold is iteratively optimized to ensure that the final skeletonized network obeys a universal property of voxelized random line networks, namely, solid-phase voxels have most likely three solid-phase neighbors in a 3 x 3 x 3 neighborhood. This optimization criterion makes our method free of user-defined parameters and the output exceptionally robust against imaging noise.

pone-0036575-g010: Statistical test of reconstruction quality.We determined the distributions of nearest obstacle distances in a binary surrogate data set and the corresponding reconstruction result. Both distributions are identical, disregarding statistical fluctuations.

Mentions:
The algorithm’s ability to correctly reconstruct random fiber networks was evaluated using surrogate data sets. We generated a set of 100 surrogate image stacks that differed widely in their network densities, point spread functions and noise levels. The quality of the reconstructed networks was evaluated quantitatively by comparing the distributions of nearest obstacle distances in the underlying binary surrogate and in the reconstructed data sets (Fig. 10). The correlation coefficient of corresponding distributions ranged from 0.87 to 0.99, with an average of 0.93.

pone-0036575-g010: Statistical test of reconstruction quality.We determined the distributions of nearest obstacle distances in a binary surrogate data set and the corresponding reconstruction result. Both distributions are identical, disregarding statistical fluctuations.

Mentions:
The algorithm’s ability to correctly reconstruct random fiber networks was evaluated using surrogate data sets. We generated a set of 100 surrogate image stacks that differed widely in their network densities, point spread functions and noise levels. The quality of the reconstructed networks was evaluated quantitatively by comparing the distributions of nearest obstacle distances in the underlying binary surrogate and in the reconstructed data sets (Fig. 10). The correlation coefficient of corresponding distributions ranged from 0.87 to 0.99, with an average of 0.93.

Bottom Line:
The size and intensity pattern of the template is automatically adapted to the input data so that the method is scale invariant and generic.Furthermore, the template matching threshold is iteratively optimized to ensure that the final skeletonized network obeys a universal property of voxelized random line networks, namely, solid-phase voxels have most likely three solid-phase neighbors in a 3 x 3 x 3 neighborhood.This optimization criterion makes our method free of user-defined parameters and the output exceptionally robust against imaging noise.

ABSTRACTWe present a method to reconstruct a disordered network of thin biopolymers, such as collagen gels, from three-dimensional (3D) image stacks recorded with a confocal microscope. The method is based on a template matching algorithm that simultaneously performs a binarization and skeletonization of the network. The size and intensity pattern of the template is automatically adapted to the input data so that the method is scale invariant and generic. Furthermore, the template matching threshold is iteratively optimized to ensure that the final skeletonized network obeys a universal property of voxelized random line networks, namely, solid-phase voxels have most likely three solid-phase neighbors in a 3 x 3 x 3 neighborhood. This optimization criterion makes our method free of user-defined parameters and the output exceptionally robust against imaging noise.